Effect of autaptic delay signal on spike-timing precision of single neuron

Abstract

Experimental and theoretical studies have reported that the precise firing of neurons is crucial for sensory representation. Autapse serves as a special synapse connecting neuron and itself, which has also been found to improve the accuracy of neuronal response. In current work, the effect of autaptic delay signal on the spike-timing precision is investigated on a single autaptic Hodgkin–Huxley neuron in the present of noise. The simulation results show that both excitatory and inhibitory autaptic signals can effectively adjust the precise spike time of neurons with noise by choosing the appropriate coupling strength g and time delay of autaptic signal τ. The g–τ parameter space is divided into two regions: one is the region where the spike-timing precision is effectively regulated; the other is the region where the neuronal firing is almost not regulated. For the excitatory and inhibitory autapse, the range of parameters causing the accuracy of neuronal firing is different. Moreover, it is also found that the mechanisms of the spike-timing precision regulation are different for the two kinds of autaptic signals.

Characterizing the role of autaptic feedback in enhancing precision of neuronal firing times

“…where v max is as in (20). An approximate solution for the mean of T in (17) as a random variable is given by solving…”

“…to obtain (20). Furthermore, we solve (13) for ⟨n(t)v(t)⟩ using the number of presynaptic vesicles at the steady state ⟨n⟩ (7) and ⟨v(0)⟩ in (31).…”

“…Such autaptic transmission has been seen in neurons both in-culture and in-vivo [11]–[16]. A combination of computational and experimental works have implicated autapses in diverse functional roles from modulating the temporal fidelity of AP firing patterns [17]– [20], inducing oscillations [21]–[23], and synchronization of neuronal networks [24]–[26]. Our approach to modeling this system leverages the Stochastic Hybrid Systems (SHS) framework that couples continuous dynamical changes in variables (such as the membrane potential) with discrete events occurring at random times (such as AP firings).…”

“…Such autaptic transmission has 1,2 Z. Vahdat and O. Gambrell are with the Department of Electrical and Computer Engineering, University of Delaware, Newark, DE USA 19716. zahravd@udel.edu,olg@udel.edu 3 A. Singh is with the Department of Electrical and Computer Engineering, Biomedical Engineering, Mathematical Sciences, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE USA 19716. absingh@udel.edu been seen in neurons both in-culture and in-vivo [11]- [16]. A combination of computational and experimental works have implicated autapses in diverse functional roles from modulating the temporal fidelity of AP firing patterns [17]- [20], inducing oscillations [21]- [23], and synchronization of neuronal networks [24]- [26]. Our approach to modeling this system leverages the Stochastic Hybrid Systems (SHS) framework that couples continuous dynamical changes in variables (such as the membrane potential) with discrete events occurring at random times (such as AP firings).…”